pub struct NonlinearConjugateGradient<P, L, B, F> { /* private fields */ }
Expand description
The nonlinear conjugate gradient is a generalization of the conjugate gradient method for nonlinear optimization problems.
References:
[0] Jorge Nocedal and Stephen J. Wright (2006). Numerical Optimization. Springer. ISBN 0-387-30303-0.
Implementations
sourceimpl<P, L, B, F> NonlinearConjugateGradient<P, L, B, F> where
P: Default,
F: ArgminFloat,
impl<P, L, B, F> NonlinearConjugateGradient<P, L, B, F> where
P: Default,
F: ArgminFloat,
sourcepub fn new(linesearch: L, beta_method: B) -> Result<Self, Error>
pub fn new(linesearch: L, beta_method: B) -> Result<Self, Error>
Constructor (Polak Ribiere Conjugate Gradient (PR-CG))
sourcepub fn restart_iters(self, iters: u64) -> Self
pub fn restart_iters(self, iters: u64) -> Self
Specifiy the number of iterations after which a restart should be performed This allows the algorithm to “forget” previous information which may not be helpful anymore.
sourcepub fn restart_orthogonality(self, v: F) -> Self
pub fn restart_orthogonality(self, v: F) -> Self
Set the value for the orthogonality measure. Setting this parameter leads to a restart of the algorithm (setting beta = 0) after two consecutive search directions are not orthogonal anymore. In other words, if this condition is met:
|\nabla f_k^T * \nabla f_{k-1}| / | \nabla f_k ||^2 >= v
A typical value for v
is 0.1.
Trait Implementations
sourceimpl<P: Clone, L: Clone, B: Clone, F: Clone> Clone for NonlinearConjugateGradient<P, L, B, F>
impl<P: Clone, L: Clone, B: Clone, F: Clone> Clone for NonlinearConjugateGradient<P, L, B, F>
sourcefn clone(&self) -> NonlinearConjugateGradient<P, L, B, F>
fn clone(&self) -> NonlinearConjugateGradient<P, L, B, F>
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl<'de, P, L, B, F> Deserialize<'de> for NonlinearConjugateGradient<P, L, B, F> where
P: Deserialize<'de>,
L: Deserialize<'de>,
B: Deserialize<'de>,
F: Deserialize<'de>,
impl<'de, P, L, B, F> Deserialize<'de> for NonlinearConjugateGradient<P, L, B, F> where
P: Deserialize<'de>,
L: Deserialize<'de>,
B: Deserialize<'de>,
F: Deserialize<'de>,
sourcefn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
sourceimpl<P, L, B, F> Serialize for NonlinearConjugateGradient<P, L, B, F> where
P: Serialize,
L: Serialize,
B: Serialize,
F: Serialize,
impl<P, L, B, F> Serialize for NonlinearConjugateGradient<P, L, B, F> where
P: Serialize,
L: Serialize,
B: Serialize,
F: Serialize,
sourceimpl<O, P, L, B, F> Solver<O> for NonlinearConjugateGradient<P, L, B, F> where
O: ArgminOp<Param = P, Output = F, Float = F>,
P: Clone + Default + Serialize + DeserializeOwned + ArgminSub<O::Param, O::Param> + ArgminDot<O::Param, O::Float> + ArgminScaledAdd<O::Param, O::Float, O::Param> + ArgminAdd<O::Param, O::Param> + ArgminMul<F, O::Param> + ArgminNorm<O::Float>,
O::Hessian: Default,
L: Clone + ArgminLineSearch<O::Param, O::Float> + Solver<O>,
B: ArgminNLCGBetaUpdate<O::Param, O::Float>,
F: ArgminFloat,
impl<O, P, L, B, F> Solver<O> for NonlinearConjugateGradient<P, L, B, F> where
O: ArgminOp<Param = P, Output = F, Float = F>,
P: Clone + Default + Serialize + DeserializeOwned + ArgminSub<O::Param, O::Param> + ArgminDot<O::Param, O::Float> + ArgminScaledAdd<O::Param, O::Float, O::Param> + ArgminAdd<O::Param, O::Param> + ArgminMul<F, O::Param> + ArgminNorm<O::Float>,
O::Hessian: Default,
L: Clone + ArgminLineSearch<O::Param, O::Float> + Solver<O>,
B: ArgminNLCGBetaUpdate<O::Param, O::Float>,
F: ArgminFloat,
sourcefn init(
&mut self,
op: &mut OpWrapper<O>,
state: &IterState<O>
) -> Result<Option<ArgminIterData<O>>, Error>
fn init(
&mut self,
op: &mut OpWrapper<O>,
state: &IterState<O>
) -> Result<Option<ArgminIterData<O>>, Error>
Initializes the algorithm Read more
sourcefn next_iter(
&mut self,
op: &mut OpWrapper<O>,
state: &IterState<O>
) -> Result<ArgminIterData<O>, Error>
fn next_iter(
&mut self,
op: &mut OpWrapper<O>,
state: &IterState<O>
) -> Result<ArgminIterData<O>, Error>
Computes one iteration of the algorithm.
sourcefn terminate_internal(&mut self, state: &IterState<O>) -> TerminationReason
fn terminate_internal(&mut self, state: &IterState<O>) -> TerminationReason
Checks whether basic termination reasons apply. Read more
sourcefn terminate(&mut self, _state: &IterState<O>) -> TerminationReason
fn terminate(&mut self, _state: &IterState<O>) -> TerminationReason
Checks whether the algorithm must be terminated
Auto Trait Implementations
impl<P, L, B, F> RefUnwindSafe for NonlinearConjugateGradient<P, L, B, F> where
B: RefUnwindSafe,
F: RefUnwindSafe,
L: RefUnwindSafe,
P: RefUnwindSafe,
impl<P, L, B, F> Send for NonlinearConjugateGradient<P, L, B, F> where
B: Send,
F: Send,
L: Send,
P: Send,
impl<P, L, B, F> Sync for NonlinearConjugateGradient<P, L, B, F> where
B: Sync,
F: Sync,
L: Sync,
P: Sync,
impl<P, L, B, F> Unpin for NonlinearConjugateGradient<P, L, B, F> where
B: Unpin,
F: Unpin,
L: Unpin,
P: Unpin,
impl<P, L, B, F> UnwindSafe for NonlinearConjugateGradient<P, L, B, F> where
B: UnwindSafe,
F: UnwindSafe,
L: UnwindSafe,
P: UnwindSafe,
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcepub fn borrow_mut(&mut self) -> &mut T
pub fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcepub fn to_owned(&self) -> T
pub fn to_owned(&self) -> T
Creates owned data from borrowed data, usually by cloning. Read more
sourcepub fn clone_into(&self, target: &mut T)
pub fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more